1
00:00:01,440 --> 00:00:05,120
Speaker 1: From the heart where innovation, money and power collie in

2
00:00:05,200 --> 00:00:10,160
Speaker 1: Silicon Valley, NBN. This is Bloomberg Technology with Caroline Hyde

3
00:00:10,160 --> 00:00:11,080
Speaker 1: and Ed Ludlow.

4
00:00:24,720 --> 00:00:27,080
Speaker 2: I'm Karin hide A, Bloomberg's World headquarters in New York,

5
00:00:27,160 --> 00:00:29,520
Speaker 2: and I'm Tim Standinbeck and for Ed Ludlow, this is

6
00:00:29,520 --> 00:00:30,639
Speaker 2: Bloomberg Technology.

7
00:00:30,760 --> 00:00:31,159
Speaker 3: Coming up.

8
00:00:31,200 --> 00:00:34,199
Speaker 2: We push ahead to the all important vote results on

9
00:00:34,280 --> 00:00:37,120
Speaker 2: Elon Musk's fifty six billion dollar pay package, and as

10
00:00:37,200 --> 00:00:38,880
Speaker 2: Tesla's agm it kicks off.

11
00:00:39,200 --> 00:00:42,520
Speaker 4: Plus, we'll turn to earnings as Broadcom surges, as AI

12
00:00:42,560 --> 00:00:44,560
Speaker 4: demand boosts its results, and we'll.

13
00:00:44,400 --> 00:00:47,040
Speaker 2: Dig into the financial terms of Apple and open AI's

14
00:00:47,120 --> 00:00:51,159
Speaker 2: partnership mid their landmark agreement. Of First, let's check in

15
00:00:51,200 --> 00:00:53,880
Speaker 2: on these markets, because we've got more fuel to this far.

16
00:00:54,000 --> 00:00:57,360
Speaker 2: We've got still record high after record high across benchmarks.

17
00:00:57,360 --> 00:00:58,880
Speaker 2: If you look at the S and P five hundred

18
00:00:58,880 --> 00:01:00,680
Speaker 2: be your fourth straight record high. On the day, we're

19
00:01:00,680 --> 00:01:02,800
Speaker 2: seeing the nastack up another tenth of a percent. Look

20
00:01:02,840 --> 00:01:04,840
Speaker 2: not the big gains that we saw yesterday. But we

21
00:01:04,920 --> 00:01:08,480
Speaker 2: digest yet another cooling of inflation with a PPI report.

22
00:01:08,480 --> 00:01:09,520
Speaker 3: The macro picture looks good.

23
00:01:09,520 --> 00:01:12,200
Speaker 2: The tenure yield falls down another three four basis points.

24
00:01:12,240 --> 00:01:14,560
Speaker 3: Let's call it forloring costs. On the downside, not so

25
00:01:14,640 --> 00:01:15,280
Speaker 3: much love.

26
00:01:15,120 --> 00:01:17,160
Speaker 2: For European stocks, though still open to trade. We're off

27
00:01:17,200 --> 00:01:20,080
Speaker 2: by one point three percent. More political anxiety happening in

28
00:01:20,120 --> 00:01:22,560
Speaker 2: Europe and perhaps, of course, digesting a big move that

29
00:01:22,560 --> 00:01:24,800
Speaker 2: we had yesterday. Move on, see what's happening in the

30
00:01:24,800 --> 00:01:27,520
Speaker 2: world of crypto tim We have been seen basically US

31
00:01:27,640 --> 00:01:29,280
Speaker 2: hold in pretty steady. We're off by about a percent,

32
00:01:29,360 --> 00:01:32,360
Speaker 2: just point sixty seven thousand, interestingly coming off of those highs.

33
00:01:32,520 --> 00:01:34,800
Speaker 2: So that moon music about risk on not quite flowing

34
00:01:34,800 --> 00:01:35,560
Speaker 2: into bitcoin today.

35
00:01:35,560 --> 00:01:37,600
Speaker 4: But what are you watching on the micro Well, speaking

36
00:01:37,640 --> 00:01:40,720
Speaker 4: of records, I'm watching shares of broad Com ticker AVGO.

37
00:01:41,080 --> 00:01:44,080
Speaker 4: They are just surging by close to fourteen percent right now.

38
00:01:44,319 --> 00:01:47,160
Speaker 4: The company did report earnings after the bill yesterday. It

39
00:01:47,160 --> 00:01:49,000
Speaker 4: beat on the top line, it beat on the bottom line.

40
00:01:49,040 --> 00:01:52,600
Speaker 4: The guidance surpassed analyst expectations. The company also announced a

41
00:01:52,640 --> 00:01:55,000
Speaker 4: ten for one stock split. It's going to take effect

42
00:01:55,080 --> 00:01:58,360
Speaker 4: next month. Investors are certainly cheering that news, sending shares

43
00:01:58,440 --> 00:02:01,760
Speaker 4: higher widely see as sort of a barometer for what's

44
00:02:01,800 --> 00:02:04,080
Speaker 4: happening in the chip space. So checking out on some

45
00:02:04,120 --> 00:02:07,480
Speaker 4: other chip companies. Nvidia also at a new record right now,

46
00:02:07,520 --> 00:02:10,799
Speaker 4: shares up more than two percent, and then watching what's

47
00:02:10,800 --> 00:02:14,320
Speaker 4: happening in the data center hardware networking when it comes

48
00:02:14,360 --> 00:02:17,720
Speaker 4: to these AI data centers. Arista Networks also right now

49
00:02:17,960 --> 00:02:21,400
Speaker 4: at a new record at more than four percent. But

50
00:02:21,560 --> 00:02:25,560
Speaker 4: speaking of shares surging, let's talk Tesla, because shares are

51
00:02:25,680 --> 00:02:28,600
Speaker 4: hired this morning, this after Elon Musk send a tweet

52
00:02:28,639 --> 00:02:31,200
Speaker 4: early this morning again as you mentioned, Caroline, the day

53
00:02:31,320 --> 00:02:36,560
Speaker 4: of that shareholder's meeting, saying that those two shareholder issues

54
00:02:36,600 --> 00:02:39,080
Speaker 4: that were being voted on have passed or will pass

55
00:02:39,160 --> 00:02:41,400
Speaker 4: by a quote wide margin. We're talking, of course, about

56
00:02:41,400 --> 00:02:44,120
Speaker 4: that twenty eighteen pay package that has been under so

57
00:02:44,240 --> 00:02:46,440
Speaker 4: much scrutiny in addition to that the idea of the

58
00:02:46,480 --> 00:02:51,079
Speaker 4: company moving its incorporation from Delaware to Texas. Shares high

59
00:02:51,200 --> 00:02:54,000
Speaker 4: right now by three point seven percent. Not much can

60
00:02:54,040 --> 00:02:57,200
Speaker 4: be said permanently about what happens with that pay package,

61
00:02:57,240 --> 00:02:59,960
Speaker 4: but this vote widely seen as sort of a barama

62
00:03:00,160 --> 00:03:02,120
Speaker 4: or a test for how people are seeing Elon Musk's

63
00:03:02,200 --> 00:03:04,600
Speaker 4: leadership at a time when the stock has been under

64
00:03:04,680 --> 00:03:07,959
Speaker 4: pressure down going into today's trade, Caroline around to thirty percent.

65
00:03:08,120 --> 00:03:10,880
Speaker 2: Let's get some expertise even further on this. Tim Dana

66
00:03:10,960 --> 00:03:13,840
Speaker 2: Hall joins us and Dana, Look, it's not going to

67
00:03:13,919 --> 00:03:17,320
Speaker 2: have an implications on the court in the here and now.

68
00:03:17,639 --> 00:03:19,520
Speaker 3: This still is all riding on a judge in the

69
00:03:19,560 --> 00:03:20,079
Speaker 3: long term.

70
00:03:20,160 --> 00:03:23,160
Speaker 2: But suddenly it holds on in a musk from walking

71
00:03:23,240 --> 00:03:23,800
Speaker 2: too soon.

72
00:03:25,560 --> 00:03:27,840
Speaker 5: Yeah, And to be honest, like, I was not expecting

73
00:03:27,880 --> 00:03:30,560
Speaker 5: Elon Musk to front run Tesla's AGM.

74
00:03:30,880 --> 00:03:32,920
Speaker 6: We thought we were going to get the vote results tonight.

75
00:03:33,040 --> 00:03:35,840
Speaker 5: But clearly Tesla is keeping very close track of the

76
00:03:35,920 --> 00:03:38,680
Speaker 5: votes that they do have, and a lot of votes

77
00:03:38,680 --> 00:03:42,120
Speaker 5: came in yesterday. The deadline two casts a vote was

78
00:03:42,800 --> 00:03:45,440
Speaker 5: late last night, and they've got the votes. I mean,

79
00:03:45,760 --> 00:03:47,600
Speaker 5: if you believe Elon's tweet, and I don't think there's

80
00:03:47,600 --> 00:03:50,200
Speaker 5: a reason not to, they have the votes on both

81
00:03:50,280 --> 00:03:53,320
Speaker 5: his pay and on the move to Texas. And what

82
00:03:53,360 --> 00:03:56,080
Speaker 5: this means is that many of the very big shareholders

83
00:03:56,280 --> 00:03:59,200
Speaker 5: that Tesla was worried about either abstained or voted yes.

84
00:03:59,320 --> 00:04:02,640
Speaker 5: And that's like a super fascinating dynamic because some of

85
00:04:02,680 --> 00:04:06,160
Speaker 5: the large funds like Vanguard actually voted no in twenty eighteen,

86
00:04:06,400 --> 00:04:10,600
Speaker 5: and they are the largest shareholder in Tesla after Musk himself.

87
00:04:10,720 --> 00:04:13,160
Speaker 4: Dan, That's what surprised me so much about this vote.

88
00:04:13,200 --> 00:04:15,520
Speaker 4: You had these large asset managers such as Vanguard that

89
00:04:15,520 --> 00:04:18,320
Speaker 4: you just mentioned, voting in favor of this. Do you

90
00:04:18,360 --> 00:04:21,240
Speaker 4: know anything about why they switched or the sort of

91
00:04:21,279 --> 00:04:23,880
Speaker 4: strange badfellows here in terms of who's voted for and

92
00:04:24,279 --> 00:04:24,960
Speaker 4: real To be clear.

93
00:04:24,839 --> 00:04:27,000
Speaker 5: Vanguard has not said anything yet. We don't know for

94
00:04:27,080 --> 00:04:28,240
Speaker 5: sure how they voted, but.

95
00:04:28,279 --> 00:04:30,120
Speaker 1: You should say this. Sorry the New York Times reporting

96
00:04:30,120 --> 00:04:31,320
Speaker 1: early this morning. Excuse me.

97
00:04:32,000 --> 00:04:33,400
Speaker 6: We have not confirmed that ourselves.

98
00:04:33,440 --> 00:04:35,200
Speaker 5: But if you look at the vote count, just a

99
00:04:35,200 --> 00:04:37,200
Speaker 5: lot of yes votes came in yesterday and they have

100
00:04:37,320 --> 00:04:41,800
Speaker 5: to be the big votes from the shareholders Vanguard, black Rock, Geode,

101
00:04:41,920 --> 00:04:47,640
Speaker 5: State Street. So yeah, it's interesting too because both Iss

102
00:04:47,680 --> 00:04:50,400
Speaker 5: and Glass Lewis, the big proxy advisory firms, were no

103
00:04:50,560 --> 00:04:53,320
Speaker 5: on the pay package. And that's why Tesla was so

104
00:04:53,880 --> 00:04:57,760
Speaker 5: aggressively courting retail investors. Tesla was worried that they didn't

105
00:04:57,800 --> 00:05:00,840
Speaker 5: have the votes from the big funds, and they actively

106
00:05:01,000 --> 00:05:01,839
Speaker 5: courting retail.

107
00:05:02,560 --> 00:05:04,040
Speaker 6: We know that they were actively.

108
00:05:03,680 --> 00:05:07,000
Speaker 5: Courting the large shareholders too, but something's turned their way

109
00:05:07,240 --> 00:05:10,279
Speaker 5: and I think they've got it I mean, so tonight

110
00:05:10,320 --> 00:05:11,400
Speaker 5: we'll get the final tally.

111
00:05:11,520 --> 00:05:13,520
Speaker 6: But I mean you're gonna see an ambulan.

112
00:05:13,279 --> 00:05:15,680
Speaker 5: Elon Musk take the stage at the AGM today.

113
00:05:15,880 --> 00:05:18,080
Speaker 4: All right, we'll certainly look forward to that again starting

114
00:05:18,120 --> 00:05:20,839
Speaker 4: at four thirty Wall Street time today, Bloomberg's Dana Hole,

115
00:05:20,880 --> 00:05:21,840
Speaker 4: thanks so much for that.

116
00:05:22,320 --> 00:05:22,480
Speaker 7: Well.

117
00:05:22,480 --> 00:05:25,280
Speaker 4: Turning now to Apple, when CEO Tim Cook announced the

118
00:05:25,320 --> 00:05:29,200
Speaker 4: company's partnership with open Ai, they didn't disclose the financial.

119
00:05:28,839 --> 00:05:30,160
Speaker 1: Terms of the agreement.

120
00:05:30,480 --> 00:05:32,520
Speaker 4: For more on what we know about the deal, Let's

121
00:05:32,520 --> 00:05:35,320
Speaker 4: bring in Bloomberg's Lynn Duan. She's here in our studios

122
00:05:35,360 --> 00:05:37,599
Speaker 4: in New York. What do we know about the financial

123
00:05:37,640 --> 00:05:40,480
Speaker 4: terms of being greading of the agreement? Just zero's all around.

124
00:05:40,720 --> 00:05:40,840
Speaker 8: Hi.

125
00:05:41,240 --> 00:05:44,560
Speaker 9: First, thank you for having me. Second, there are no

126
00:05:44,680 --> 00:05:45,520
Speaker 9: financial terms.

127
00:05:45,560 --> 00:05:46,080
Speaker 10: Apparently.

128
00:05:46,440 --> 00:05:48,279
Speaker 9: It's funny because I feel like that was the biggest

129
00:05:48,360 --> 00:05:50,839
Speaker 9: question on that day when they announced this partnership.

130
00:05:50,880 --> 00:05:53,000
Speaker 10: It was like, Okay, so who's paying who?

131
00:05:53,000 --> 00:05:54,080
Speaker 3: Who's making money here?

132
00:05:54,279 --> 00:05:57,160
Speaker 9: It turns out there is no money exchanging hands at

133
00:05:57,240 --> 00:06:02,279
Speaker 9: least at the onset right. Actually happening is according to

134
00:06:02,560 --> 00:06:06,600
Speaker 9: Apple reporter Mark German, sources is Apple believes that there

135
00:06:06,680 --> 00:06:10,480
Speaker 9: is enough value, maybe even more value than monetary value

136
00:06:10,960 --> 00:06:14,760
Speaker 9: to open ai and having its products on a ton

137
00:06:14,800 --> 00:06:18,000
Speaker 9: of Apple devices across the board. The promotion of their

138
00:06:18,040 --> 00:06:21,840
Speaker 9: product there offers them a business proposition if people want

139
00:06:21,839 --> 00:06:25,440
Speaker 9: to say, go from the free version of open ai

140
00:06:25,839 --> 00:06:28,560
Speaker 9: to the paid version for twenty dollars a month. So

141
00:06:28,640 --> 00:06:31,320
Speaker 9: that's the proposition to open ai and for Apple it's

142
00:06:31,320 --> 00:06:35,800
Speaker 9: clearly a value add in allowing users like me and

143
00:06:35,880 --> 00:06:38,400
Speaker 9: you to be able to access open ai and all

144
00:06:38,440 --> 00:06:41,880
Speaker 9: of the tools the i generated content that it has

145
00:06:42,279 --> 00:06:43,400
Speaker 9: on our Apple devices.

146
00:06:43,839 --> 00:06:46,360
Speaker 2: That has been some cost apart from nal Musk's aya

147
00:06:46,640 --> 00:06:49,640
Speaker 2: and claim that he'll ban Apple iPhones going into any

148
00:06:49,680 --> 00:06:54,120
Speaker 2: of his companies, but this integration will actually cost open

149
00:06:54,160 --> 00:06:56,200
Speaker 2: ai something, right because well, they've got to pay for

150
00:06:56,240 --> 00:06:57,800
Speaker 2: the compute if everyone selves using.

151
00:06:57,680 --> 00:07:01,440
Speaker 9: It, that's right, And the the idea behind it is

152
00:07:01,440 --> 00:07:05,400
Speaker 9: that in the long term, this cost, this investment that

153
00:07:05,400 --> 00:07:08,839
Speaker 9: they're making will pay off both for Apple and for

154
00:07:08,960 --> 00:07:11,680
Speaker 9: open Ai. For Apple, I think that the long term

155
00:07:11,720 --> 00:07:15,120
Speaker 9: advantage here. The reason why they are making partnerships with

156
00:07:15,200 --> 00:07:17,680
Speaker 9: open ai and are trying to make partnerships with other

157
00:07:17,720 --> 00:07:18,560
Speaker 9: AI developers.

158
00:07:18,560 --> 00:07:19,240
Speaker 10: By the way, this.

159
00:07:19,200 --> 00:07:23,160
Speaker 9: Is not an exclusive arrangement, is that hopefully one day

160
00:07:23,200 --> 00:07:26,520
Speaker 9: they will be able to monetize it through revenue sharing agreements.

161
00:07:26,800 --> 00:07:29,640
Speaker 9: So anytime one of these AI developers is able to

162
00:07:29,680 --> 00:07:33,400
Speaker 9: sell a subscription through an Apple device, hopefully Apple will

163
00:07:33,440 --> 00:07:34,560
Speaker 9: get a cut from it.

164
00:07:34,760 --> 00:07:35,920
Speaker 10: That's what Apple hopes.

165
00:07:36,280 --> 00:07:38,960
Speaker 9: And as far as the AI developers go, they are

166
00:07:38,960 --> 00:07:41,120
Speaker 9: willing to give this investment in the hopes that it

167
00:07:41,160 --> 00:07:43,920
Speaker 9: will generate more revenue. And mind you, this is a

168
00:07:43,920 --> 00:07:46,560
Speaker 9: big push for open ai right now. They are under

169
00:07:46,600 --> 00:07:49,480
Speaker 9: a lot of pressure to show that they can not

170
00:07:49,600 --> 00:07:53,360
Speaker 9: only generate revenue, but that they can generate enough revenue

171
00:07:53,520 --> 00:07:55,920
Speaker 9: to turn a profit. And that's what they really need

172
00:07:55,960 --> 00:07:58,600
Speaker 9: in order to keep all this AI compute going is

173
00:07:58,680 --> 00:08:02,200
Speaker 9: more and more money me into the institution to fund it.

174
00:08:02,240 --> 00:08:04,360
Speaker 7: Well, they're not out of data center capacity.

175
00:08:04,080 --> 00:08:04,960
Speaker 3: Larder data center.

176
00:08:05,120 --> 00:08:07,480
Speaker 2: Maybe they're saving a little bit by Apple not getting

177
00:08:07,480 --> 00:08:10,800
Speaker 2: a cut on the chatchypt app for now, but certainly

178
00:08:10,880 --> 00:08:12,680
Speaker 2: lots of way that this can be earning some bucks.

179
00:08:12,680 --> 00:08:13,120
Speaker 3: We thank you.

180
00:08:13,160 --> 00:08:16,760
Speaker 2: That was a perfect summary, Ninduan of Bloomberg. Meanwhile, coming up,

181
00:08:16,880 --> 00:08:19,960
Speaker 2: we'll go break down broad COM's pretty sensational results. Actually

182
00:08:20,080 --> 00:08:22,560
Speaker 2: shares surging as a timent outlined a little bit earlier.

183
00:08:22,880 --> 00:08:25,640
Speaker 2: It's all on guess what AI demand. But we want

184
00:08:25,640 --> 00:08:28,240
Speaker 2: to go back actually to what's been happening overseas. A

185
00:08:28,320 --> 00:08:31,240
Speaker 2: BYD Chinese ev maker of course, actually traded here.

186
00:08:31,120 --> 00:08:32,360
Speaker 3: In the US for some ADRs.

187
00:08:32,600 --> 00:08:34,960
Speaker 2: We're higher on the day, and that's as we start

188
00:08:34,960 --> 00:08:38,199
Speaker 2: to digest the EU tarras, which initially was a shock,

189
00:08:38,600 --> 00:08:40,920
Speaker 2: but actually we think this perhaps is slightly less of

190
00:08:40,960 --> 00:08:43,440
Speaker 2: a taroff than having been anticipated seventeen percent of BYD

191
00:08:43,640 --> 00:08:45,680
Speaker 2: and actually they can stomach it if they're going to

192
00:08:45,679 --> 00:08:47,040
Speaker 2: continue to sell into Europe.

193
00:08:47,240 --> 00:09:08,640
Speaker 8: This is a broom make technology.

194
00:09:00,040 --> 00:09:02,680
Speaker 4: Well shares a chip supplier Broadcam, surging after the company

195
00:09:02,840 --> 00:09:06,559
Speaker 4: reported earnings that were boosted by demand for AI products.

196
00:09:06,760 --> 00:09:09,160
Speaker 4: Joining us now for more is Kun John Sabani of

197
00:09:09,280 --> 00:09:14,120
Speaker 4: Bloomberg Intelligence. Kunjohn, These results just exceeded all analysts expectations

198
00:09:14,120 --> 00:09:16,480
Speaker 4: on the top line the bottom line guidance as well.

199
00:09:17,040 --> 00:09:19,880
Speaker 4: Remind us of the space that broadcomplays and a huge

200
00:09:19,920 --> 00:09:22,480
Speaker 4: Apple supplier of course, But tell us about the AI

201
00:09:22,640 --> 00:09:23,280
Speaker 4: element here.

202
00:09:24,240 --> 00:09:26,199
Speaker 10: Yeah, So they have two core AI businesses.

203
00:09:26,240 --> 00:09:31,360
Speaker 11: One is the AC accelerators, which is alternative to merchant

204
00:09:31,440 --> 00:09:35,040
Speaker 11: GPU what someone like an Nvidia provides, so mainly focused

205
00:09:35,040 --> 00:09:39,760
Speaker 11: on the large cloud players and hyperscalers like Google, Meta, Microsoft, etc.

206
00:09:40,280 --> 00:09:43,559
Speaker 11: And the second is the AI networking or the overall

207
00:09:43,600 --> 00:09:46,920
Speaker 11: networking business, which until now was the biggest piece, which

208
00:09:46,960 --> 00:09:50,760
Speaker 11: is where basically the pipeline that runs the data center

209
00:09:50,840 --> 00:09:54,000
Speaker 11: runs on. So anything outside of the cpugpun memory that's

210
00:09:54,080 --> 00:09:56,760
Speaker 11: basically all the networking components Qrinjin.

211
00:09:57,440 --> 00:10:01,360
Speaker 2: We have clearly some competitive inching into the space. When

212
00:10:01,360 --> 00:10:04,160
Speaker 2: you think of Nvideo, it's a good strong number two.

213
00:10:04,200 --> 00:10:06,079
Speaker 3: But what's also interesting is they're taking a leaf out

214
00:10:06,080 --> 00:10:07,760
Speaker 3: of Video's book. They're doing a stock split.

215
00:10:09,920 --> 00:10:12,360
Speaker 11: Yeah, I mean that seems to be the trend these days.

216
00:10:12,400 --> 00:10:14,760
Speaker 11: You beat on good AI results into a stock split

217
00:10:14,880 --> 00:10:17,280
Speaker 11: and you see what happens, double digit stock good. But

218
00:10:17,640 --> 00:10:19,840
Speaker 11: I think from a combination point of view, you know,

219
00:10:20,040 --> 00:10:23,040
Speaker 11: from in the near term, in terms of the use case,

220
00:10:23,120 --> 00:10:26,600
Speaker 11: we don't think they're really competing with each other because

221
00:10:26,600 --> 00:10:29,040
Speaker 11: the use case right now are different and both of

222
00:10:29,080 --> 00:10:31,880
Speaker 11: them there's no hurdles to growth for both companies. But

223
00:10:31,920 --> 00:10:35,160
Speaker 11: I think in the long term it definitely will compete,

224
00:10:35,480 --> 00:10:37,400
Speaker 11: not in the use case, but more in the wallet

225
00:10:37,440 --> 00:10:42,120
Speaker 11: share because there are clouds providers and hyperscalers would have

226
00:10:42,200 --> 00:10:43,360
Speaker 11: limited budget.

227
00:10:43,040 --> 00:10:46,880
Speaker 2: To spend and designing their own chips slowly but surely

228
00:10:47,480 --> 00:10:49,959
Speaker 2: really interesting countries. Survanning, we thank you so much. A

229
00:10:50,040 --> 00:10:53,760
Speaker 2: Boomberg intelligence running up on what is happening for broad Coom.

230
00:10:53,840 --> 00:10:56,440
Speaker 2: But let's broaden ow is all the play and AI

231
00:10:56,559 --> 00:10:59,199
Speaker 2: about the infrastructure space, the picks and shovels, the actual chips,

232
00:10:59,200 --> 00:11:01,800
Speaker 2: So what about the applications, what about the large language models?

233
00:11:01,840 --> 00:11:04,240
Speaker 2: You've got the perfect voice to really dovetail that with

234
00:11:04,520 --> 00:11:08,400
Speaker 2: federal policy Reserve. Well, it is Joe Joo, his Millennia

235
00:11:08,480 --> 00:11:10,920
Speaker 2: Capital managing partner. Joe used to work over at the FED.

236
00:11:11,240 --> 00:11:13,800
Speaker 2: So you're the perfect person to sort of outline whether

237
00:11:13,960 --> 00:11:16,720
Speaker 2: in this environment where eventually we might get one, maybe

238
00:11:16,760 --> 00:11:19,600
Speaker 2: even two cuts this year, in terms of overall policy,

239
00:11:19,880 --> 00:11:22,480
Speaker 2: you should be putting yet more money into a broad coom,

240
00:11:22,480 --> 00:11:24,400
Speaker 2: into an nvideo, into some other AI plays.

241
00:11:25,280 --> 00:11:25,480
Speaker 10: Yeah.

242
00:11:25,520 --> 00:11:28,880
Speaker 12: Look, you know, by taking a step back, I think

243
00:11:28,920 --> 00:11:31,760
Speaker 12: that there's we're in this huge AI build out, and

244
00:11:32,120 --> 00:11:33,720
Speaker 12: there's a couple of ways you can employ this market.

245
00:11:33,800 --> 00:11:36,960
Speaker 12: You could you could participate in a semis etf you

246
00:11:36,960 --> 00:11:39,520
Speaker 12: could pick the winners. But I think what I'm what

247
00:11:39,559 --> 00:11:42,760
Speaker 12: I'm thinking my head is I think there's some naysayers

248
00:11:42,800 --> 00:11:45,880
Speaker 12: who are saying, well, this AI build out is we're

249
00:11:45,880 --> 00:11:47,240
Speaker 12: going to see the peak of the curve in the

250
00:11:47,240 --> 00:11:50,040
Speaker 12: next couple of years. I personally think that this hyperworld

251
00:11:50,080 --> 00:11:52,520
Speaker 12: phase in the in the semis will probably go on

252
00:11:52,600 --> 00:11:54,760
Speaker 12: for years, just because I think what the street is

253
00:11:54,760 --> 00:11:57,200
Speaker 12: not saying is that there's going to be a lot

254
00:11:57,240 --> 00:12:00,120
Speaker 12: of incremental demand from some of the tech blaggers that

255
00:12:00,200 --> 00:12:02,080
Speaker 12: needs to catch up on AI. There's going to be

256
00:12:02,160 --> 00:12:06,120
Speaker 12: foreign buyers from let's say Europe and emerging markets. And lastly,

257
00:12:06,120 --> 00:12:08,480
Speaker 12: I think what we're also thinking is there's going to

258
00:12:08,520 --> 00:12:11,400
Speaker 12: be a lot of training and retraining of AI. AI

259
00:12:11,480 --> 00:12:13,880
Speaker 12: is like software every time on your iPhone, You've got

260
00:12:13,960 --> 00:12:15,120
Speaker 12: to update your software every.

261
00:12:14,920 --> 00:12:16,480
Speaker 7: Single every single week.

262
00:12:16,679 --> 00:12:20,120
Speaker 12: So in AI, as their new data becoming available, you have.

263
00:12:20,000 --> 00:12:21,640
Speaker 7: To update the AI, you have to retrain.

264
00:12:21,720 --> 00:12:24,439
Speaker 12: So I personally think that the AI at the infratrate

265
00:12:24,840 --> 00:12:25,720
Speaker 12: as they had a lot.

266
00:12:25,640 --> 00:12:27,080
Speaker 7: Of room to go.

267
00:12:27,320 --> 00:12:30,160
Speaker 4: Well, Joe, I'm sticking with your experience on the Federal

268
00:12:30,160 --> 00:12:32,400
Speaker 4: Reserve because look, at the end of the day, rates

269
00:12:32,400 --> 00:12:34,760
Speaker 4: are pretty much everything when we're talking about this stuff.

270
00:12:35,040 --> 00:12:37,760
Speaker 4: I'm wondering how you're thinking about these high flying tech

271
00:12:37,800 --> 00:12:40,319
Speaker 4: companies and what exactly is priced in with regard to

272
00:12:40,520 --> 00:12:43,720
Speaker 4: rate cuts. Are we going to see still moves higher

273
00:12:44,040 --> 00:12:46,760
Speaker 4: even after the Fed does cut rates if it does

274
00:12:46,800 --> 00:12:48,880
Speaker 4: cut rates this year, or is that already priced in.

275
00:12:49,760 --> 00:12:52,000
Speaker 12: Look as a former staffer now and a founder of

276
00:12:52,120 --> 00:12:55,560
Speaker 12: my a firm, I take the view that it's important

277
00:12:55,559 --> 00:12:58,720
Speaker 12: to look at the long term directions of inter treates,

278
00:12:58,840 --> 00:13:02,280
Speaker 12: you know, in economic saying like, we're ninety percent confident,

279
00:13:02,320 --> 00:13:04,640
Speaker 12: we're ninety five percent competent, we're probably ninety percent competent

280
00:13:05,000 --> 00:13:07,040
Speaker 12: that there's going to be at least one rate cut

281
00:13:07,120 --> 00:13:09,640
Speaker 12: sometime in the next nine months. So whether it starts

282
00:13:09,640 --> 00:13:13,160
Speaker 12: in July, September, December, work in January, I think it

283
00:13:13,240 --> 00:13:15,880
Speaker 12: is not as important to me as an investor because

284
00:13:15,920 --> 00:13:18,000
Speaker 12: you know the directions of the rates are coming down.

285
00:13:18,040 --> 00:13:20,440
Speaker 12: So what that means what you can do is to

286
00:13:20,600 --> 00:13:22,320
Speaker 12: put you know, I think there's one more thing that

287
00:13:22,360 --> 00:13:24,360
Speaker 12: I think the street often gets run out, and I'll

288
00:13:24,360 --> 00:13:26,439
Speaker 12: say this for the record, is I think the street

289
00:13:26,480 --> 00:13:29,360
Speaker 12: put puts a lot of emphasis on the back plod chart,

290
00:13:29,440 --> 00:13:31,760
Speaker 12: and I think what I would do is probably put

291
00:13:31,800 --> 00:13:33,880
Speaker 12: sixty percent of your weight on the BA plot chart

292
00:13:34,120 --> 00:13:36,559
Speaker 12: and put forty percent of your own research analysis into

293
00:13:36,600 --> 00:13:39,840
Speaker 12: the thinking. So you come up with your own framework

294
00:13:40,040 --> 00:13:41,720
Speaker 12: to what you think it's going to be the pack

295
00:13:41,720 --> 00:13:43,920
Speaker 12: of interest rates, and then you put that into your

296
00:13:43,920 --> 00:13:46,600
Speaker 12: financial model, your DCF model on Excel, and then you

297
00:13:46,600 --> 00:13:48,520
Speaker 12: can sort of figure out, you know, what you think

298
00:13:48,600 --> 00:13:50,680
Speaker 12: is fair value on the S and P, on MAST

299
00:13:51,120 --> 00:13:51,880
Speaker 12: and on the stocks.

300
00:13:52,200 --> 00:13:54,600
Speaker 7: So for me, I just had to take a step back.

301
00:13:54,840 --> 00:13:57,600
Speaker 12: I'm less worried about whether the cut starts in September

302
00:13:57,679 --> 00:13:59,600
Speaker 12: with December, but I know I think there's a rate

303
00:13:59,679 --> 00:14:02,480
Speaker 12: coming coming so that I'll just put back into my

304
00:14:02,520 --> 00:14:04,160
Speaker 12: model and then they'll be able to help me make

305
00:14:04,360 --> 00:14:05,720
Speaker 12: better decisions as an investor.

306
00:14:06,200 --> 00:14:09,080
Speaker 2: You're betting on the trend and you're betting actually on

307
00:14:09,160 --> 00:14:12,800
Speaker 2: private companies. I'm thankful Lanthropic you put money into some

308
00:14:12,840 --> 00:14:14,120
Speaker 2: of the large language models.

309
00:14:14,440 --> 00:14:15,400
Speaker 3: Dissect for us.

310
00:14:15,440 --> 00:14:18,680
Speaker 2: Where the money then now is allocated in this space?

311
00:14:19,040 --> 00:14:20,600
Speaker 3: Is it the large language models?

312
00:14:20,600 --> 00:14:22,720
Speaker 2: At what point do we start seeing more money allocated

313
00:14:22,720 --> 00:14:25,360
Speaker 2: to the applications the use of these large language models.

314
00:14:26,200 --> 00:14:30,320
Speaker 12: Yeah, So you know, there's sort of six broader themes

315
00:14:30,480 --> 00:14:33,320
Speaker 12: of this AI in value chain. In my head, if

316
00:14:33,360 --> 00:14:36,280
Speaker 12: you start from the very bottom, right, you have electricity,

317
00:14:36,640 --> 00:14:40,240
Speaker 12: you have data centers, you have you have cooling centers,

318
00:14:40,480 --> 00:14:43,720
Speaker 12: and then on top of that you'll have CHIPS's the

319
00:14:43,760 --> 00:14:45,760
Speaker 12: semi use and you have the data owners like the

320
00:14:46,040 --> 00:14:49,480
Speaker 12: Max seven, and then on the path you have the

321
00:14:49,560 --> 00:14:52,200
Speaker 12: large language models. And then you need human talent. You

322
00:14:52,240 --> 00:14:54,480
Speaker 12: need smart people to train the AI and work on it.

323
00:14:54,680 --> 00:14:56,360
Speaker 12: And the lastly you need the app. You know, we're

324
00:14:56,400 --> 00:14:59,120
Speaker 12: going to build the applications. I think as an investor,

325
00:14:59,240 --> 00:15:01,400
Speaker 12: you know you can have a They played this AI

326
00:15:01,440 --> 00:15:04,600
Speaker 12: trade in public markets by buying public stock to QQQ,

327
00:15:05,200 --> 00:15:06,280
Speaker 12: But in private.

328
00:15:05,920 --> 00:15:08,160
Speaker 7: Markets, what we're doing is sort of like we're sort.

329
00:15:07,960 --> 00:15:09,960
Speaker 12: Of an ETF in the private markets, and that we're

330
00:15:10,280 --> 00:15:13,840
Speaker 12: invested in entroffic, WM, the cohere because these are the

331
00:15:13,880 --> 00:15:16,640
Speaker 12: innovations companies happening in the private markets.

332
00:15:16,800 --> 00:15:18,600
Speaker 7: But there's also other ways to play this AI.

333
00:15:18,480 --> 00:15:20,680
Speaker 12: Trade, because you could also be investing in utility, you

334
00:15:20,680 --> 00:15:22,360
Speaker 12: could be buying data centers.

335
00:15:22,600 --> 00:15:24,400
Speaker 7: So I think there's no different waste. But I think

336
00:15:24,400 --> 00:15:25,920
Speaker 7: the fundamental.

337
00:15:26,760 --> 00:15:30,880
Speaker 12: Phasis in my head is that it's a foregun conclusion

338
00:15:31,320 --> 00:15:33,120
Speaker 12: what the AI is real. In fact, I think AI

339
00:15:33,240 --> 00:15:35,360
Speaker 12: is going to be you know, like software square, the

340
00:15:35,360 --> 00:15:38,000
Speaker 12: Internet cube, if you will, excuse me, but I think

341
00:15:38,200 --> 00:15:39,320
Speaker 12: the biggest question in my head.

342
00:15:39,240 --> 00:15:41,359
Speaker 7: Is actually AI regulation. But that aside.

343
00:15:41,680 --> 00:15:43,120
Speaker 12: I think when you take a step back and you

344
00:15:43,160 --> 00:15:45,480
Speaker 12: look at some of the companies in this AI trade,

345
00:15:45,600 --> 00:15:48,160
Speaker 12: we're at the very beginning where you know, you can

346
00:15:48,200 --> 00:15:50,880
Speaker 12: think of it like a two year old baby. And

347
00:15:50,520 --> 00:15:53,280
Speaker 12: and so the saying is, in playing words, if this baby,

348
00:15:53,320 --> 00:15:55,760
Speaker 12: a two year old can do what it can do,

349
00:15:55,920 --> 00:15:58,920
Speaker 12: imagine when that baby matures as an.

350
00:15:58,800 --> 00:16:01,120
Speaker 7: Adult, how much more where that person can do?

351
00:16:01,400 --> 00:16:03,680
Speaker 4: Okay, well, as a venture capitalist, you're thinking about that

352
00:16:03,720 --> 00:16:06,120
Speaker 4: baby as an adult. So very briefly, paint a picture

353
00:16:06,160 --> 00:16:09,000
Speaker 4: for us about what AI can do when it's nineteen

354
00:16:09,040 --> 00:16:09,920
Speaker 4: years old.

355
00:16:10,400 --> 00:16:13,520
Speaker 12: Yeah, then we're talking about the six layer, which is

356
00:16:13,680 --> 00:16:16,240
Speaker 12: the application layer. Right, So there's you know, I guess

357
00:16:16,560 --> 00:16:19,920
Speaker 12: the industry phrase theory is called vertical software, which is

358
00:16:19,920 --> 00:16:24,320
Speaker 12: you can apply AI in cybersecurity, in automation, in robotics.

359
00:16:24,560 --> 00:16:26,600
Speaker 12: You know, there's a company called Figure ai that's combining

360
00:16:26,720 --> 00:16:30,560
Speaker 12: robotics the AI. We're invested in a AI cybersecurity company

361
00:16:30,600 --> 00:16:31,400
Speaker 12: called Deep Instinct.

362
00:16:31,880 --> 00:16:32,960
Speaker 7: But you can also apply.

363
00:16:32,760 --> 00:16:36,360
Speaker 12: AI to pharmer research to know, many other fields that

364
00:16:36,360 --> 00:16:38,680
Speaker 12: we haven't even thought about. There's one thing I got

365
00:16:38,720 --> 00:16:41,560
Speaker 12: to say that's really really important. When we think back

366
00:16:41,680 --> 00:16:45,240
Speaker 12: at the major technological innovations of the last two hundred years,

367
00:16:45,520 --> 00:16:49,400
Speaker 12: whether it was electricity, the engine, the Internet, how did

368
00:16:49,400 --> 00:16:51,840
Speaker 12: we as a humanity create that. We did it with

369
00:16:51,960 --> 00:16:56,360
Speaker 12: human intelligence, people working together. What happens now when you

370
00:16:56,400 --> 00:17:00,520
Speaker 12: can recreate intelligence, you can recreate many many atimics edicines.

371
00:17:00,720 --> 00:17:04,040
Speaker 12: So there's many infinite pathibilities that we can create. So

372
00:17:04,080 --> 00:17:07,280
Speaker 12: what I'm trying to say is it's really I think

373
00:17:07,280 --> 00:17:10,760
Speaker 12: about how we nurture this technology in a way that's

374
00:17:10,840 --> 00:17:13,960
Speaker 12: going to help us, help helps the community move forward

375
00:17:14,359 --> 00:17:15,920
Speaker 12: and grow in a way that's responsible.

376
00:17:16,160 --> 00:17:19,080
Speaker 4: Joja Ou of Millennia Capital, thanks so much for joining us.

377
00:17:19,080 --> 00:17:24,560
Speaker 4: Certainly do appreciate it.

378
00:17:27,480 --> 00:17:28,440
Speaker 3: I'm now for talking tech.

379
00:17:28,480 --> 00:17:33,720
Speaker 2: First startup al Alpha says, look, AI must grow beyond chatboots.

380
00:17:33,160 --> 00:17:34,480
Speaker 3: In order to be profitable.

381
00:17:34,520 --> 00:17:38,040
Speaker 2: Speaking to Bloomberg, the CEO Jonas Andreulis says businesses need

382
00:17:38,119 --> 00:17:40,679
Speaker 2: more specialized models other than Ela lambs that power the

383
00:17:40,720 --> 00:17:42,800
Speaker 2: likes to chutch ept. He went on to compare the

384
00:17:42,920 --> 00:17:46,119
Speaker 2: current AI craze to the likes of the dot com

385
00:17:46,200 --> 00:17:49,920
Speaker 2: bubble of the late nineties, plus Norway hits meta with

386
00:17:50,080 --> 00:17:52,560
Speaker 2: a complaint over their plans to use images and posts

387
00:17:52,600 --> 00:17:55,119
Speaker 2: from Facebook and Instagram to train AI models. Now, the

388
00:17:55,119 --> 00:17:58,560
Speaker 2: Norwegian Consumer Council says Meta has made it quit deliberately

389
00:17:58,640 --> 00:18:01,720
Speaker 2: cumbersome to opt out of the AI scraping process, which

390
00:18:01,720 --> 00:18:02,800
Speaker 2: would violate EU.

391
00:18:02,760 --> 00:18:03,720
Speaker 3: Data protection rules.

392
00:18:04,359 --> 00:18:07,639
Speaker 2: Meanwhile, Open Ai CEO Sam Altman says the company is

393
00:18:07,680 --> 00:18:09,840
Speaker 2: on a pace for a three point four billion dollar

394
00:18:10,000 --> 00:18:12,680
Speaker 2: annual revenue now. According to a person familiar with the matter,

395
00:18:12,720 --> 00:18:16,440
Speaker 2: Sam Altman's addressing staff and in an all hands meeting Wednesday,

396
00:18:16,440 --> 00:18:18,639
Speaker 2: he added that the company was on track to generate

397
00:18:18,640 --> 00:18:21,800
Speaker 2: two hundred million dollars in this AI partnership through Microsoft

398
00:18:21,840 --> 00:18:22,160
Speaker 2: A zero.

399
00:18:22,560 --> 00:18:25,720
Speaker 4: Well, sticking with AI, Adobe out with earnings after the bell,

400
00:18:26,040 --> 00:18:29,160
Speaker 4: where investors will be focusing on the company's AI progress

401
00:18:29,200 --> 00:18:31,600
Speaker 4: and if customers are spending for it.

402
00:18:31,720 --> 00:18:32,480
Speaker 1: Joining us now is.

403
00:18:32,560 --> 00:18:36,720
Speaker 4: Bloomberg's and Brody Ford Brody good to see you this morning.

404
00:18:37,840 --> 00:18:42,280
Speaker 4: I'm wondering where where the context is here because shares

405
00:18:42,280 --> 00:18:45,440
Speaker 4: are down more than twenty percent going into the print

406
00:18:45,520 --> 00:18:48,399
Speaker 4: later today, and the company plays in an interesting space

407
00:18:48,440 --> 00:18:50,960
Speaker 4: because it provides AI services, but at the same time

408
00:18:51,320 --> 00:18:54,280
Speaker 4: it's software as a service, which has just been beat

409
00:18:54,359 --> 00:18:57,000
Speaker 4: up this year as companies have shifted their budgets more

410
00:18:57,080 --> 00:18:59,640
Speaker 4: to hardware. What has beat up the company more going

411
00:18:59,680 --> 00:19:02,160
Speaker 4: into ourn Is it competition or is it a lack

412
00:19:02,200 --> 00:19:04,359
Speaker 4: of spend when it comes to software and services?

413
00:19:05,280 --> 00:19:08,520
Speaker 13: Yeah, Adobe is one of the more interesting AI debates

414
00:19:08,560 --> 00:19:11,200
Speaker 13: out there because, like all software, it's been the question

415
00:19:11,280 --> 00:19:14,359
Speaker 13: of are they benefiting from AI or are they getting

416
00:19:14,400 --> 00:19:17,639
Speaker 13: replaced by AI? You know, Adobe is famous for their

417
00:19:17,760 --> 00:19:23,080
Speaker 13: creative portfolio Photoshop video editors, and they've introduced new features

418
00:19:23,119 --> 00:19:26,359
Speaker 13: to be able to generate imagery in there. The key

419
00:19:26,400 --> 00:19:30,040
Speaker 13: fear for investors is that over time with AI, if

420
00:19:30,040 --> 00:19:32,840
Speaker 13: I'm able to type in you know, I don't know

421
00:19:33,000 --> 00:19:36,040
Speaker 13: TV show anchors talking about tech, am I really going

422
00:19:36,119 --> 00:19:38,359
Speaker 13: to need you know, Photoshop to do that?

423
00:19:38,400 --> 00:19:40,320
Speaker 14: Am I gonna need a video editor to do that?

424
00:19:40,720 --> 00:19:43,800
Speaker 13: And are small business is going to stop using these

425
00:19:43,840 --> 00:19:44,840
Speaker 13: licenses so much?

426
00:19:44,960 --> 00:19:47,720
Speaker 14: Right? And so Adobe has spoken a big game.

427
00:19:47,600 --> 00:19:51,280
Speaker 13: About AI uplift for the probably over a year now,

428
00:19:51,920 --> 00:19:54,080
Speaker 13: it really hasn't showed up in the numbers, and so

429
00:19:54,200 --> 00:19:57,160
Speaker 13: investors are starting to feel like, hold up, maybe these

430
00:19:57,240 --> 00:19:59,719
Speaker 13: numbers aren't going to really come buddy.

431
00:20:00,080 --> 00:20:02,240
Speaker 2: The numbers ain't that bad in terms of growth of

432
00:20:02,240 --> 00:20:05,760
Speaker 2: still ten percent for revenue expanded and actually consistently going

433
00:20:05,800 --> 00:20:08,760
Speaker 2: to be about ten eleven percent for the next few years.

434
00:20:09,000 --> 00:20:11,800
Speaker 2: So where is the anxiety eventually going to show U

435
00:20:11,800 --> 00:20:14,040
Speaker 2: where we're going to actually see them eroding some of

436
00:20:14,040 --> 00:20:14,840
Speaker 2: that revenue game.

437
00:20:16,080 --> 00:20:18,600
Speaker 14: Yeah, it's it is for sure a long term risk.

438
00:20:18,680 --> 00:20:20,680
Speaker 13: I mean, I haven't seen a whole lot of evidence

439
00:20:20,920 --> 00:20:25,120
Speaker 13: of companies, for example, ending their Adobe licenses and saying

440
00:20:25,160 --> 00:20:28,080
Speaker 13: I'm just going to use Dolli to generate images. The

441
00:20:28,160 --> 00:20:31,159
Speaker 13: anxiety is that there's a lot of new spending going

442
00:20:31,200 --> 00:20:34,880
Speaker 13: toward these AI startups, and in theory that new spending

443
00:20:34,920 --> 00:20:37,880
Speaker 13: should have been going towards the software incumbent like Adobe.

444
00:20:38,320 --> 00:20:41,320
Speaker 13: I mean, it is such a small metric investor's focused

445
00:20:41,359 --> 00:20:43,560
Speaker 13: on here with one of the craziest acronyms, it's like

446
00:20:44,080 --> 00:20:47,719
Speaker 13: net new annual recurring revenue for their digital media segment,

447
00:20:48,359 --> 00:20:52,280
Speaker 13: and that has gone down bit by bit again because

448
00:20:52,320 --> 00:20:55,160
Speaker 13: there's this sense of like, hey, maybe they are not

449
00:20:55,240 --> 00:20:58,199
Speaker 13: getting new workloads that are ending up in the hands

450
00:20:58,200 --> 00:21:00,520
Speaker 13: of Runway or Dolli or mid journey.

451
00:21:01,080 --> 00:21:04,480
Speaker 4: Hey Brody, I'm wondering what you see or analyst rather

452
00:21:04,640 --> 00:21:07,000
Speaker 4: as the biggest competition out there right now from the

453
00:21:07,119 --> 00:21:11,320
Speaker 4: upstarts that exist. Is it the lms or is it

454
00:21:11,359 --> 00:21:13,879
Speaker 4: the bigger companies that are starting to adopt what the

455
00:21:14,119 --> 00:21:16,120
Speaker 4: llms kind of started doing earlier.

456
00:21:17,160 --> 00:21:19,800
Speaker 13: It's definitely not the bigger companies. I mean Adobe is

457
00:21:19,800 --> 00:21:23,480
Speaker 13: thecumbent here. I think the biggest anxiety point is Kanva

458
00:21:23,560 --> 00:21:24,640
Speaker 13: and has been for a long time.

459
00:21:24,680 --> 00:21:26,400
Speaker 14: You know, that great Australian startup.

460
00:21:26,760 --> 00:21:29,439
Speaker 13: They're known for making the templates that you might end

461
00:21:29,520 --> 00:21:32,320
Speaker 13: up seeing on Instagram or you might see your friend's wedding.

462
00:21:32,400 --> 00:21:34,679
Speaker 13: You know, they've just made it a lot simpler to

463
00:21:34,800 --> 00:21:37,879
Speaker 13: do quick design, it's a little cheaper, it's all on

464
00:21:37,880 --> 00:21:38,240
Speaker 13: the web.

465
00:21:38,280 --> 00:21:41,320
Speaker 14: I mean that's who Adobe investors have been anxious about

466
00:21:41,320 --> 00:21:41,840
Speaker 14: for a while.

467
00:21:42,119 --> 00:21:42,399
Speaker 3: Yeah.

468
00:21:42,480 --> 00:21:44,000
Speaker 14: On the upstart side.

469
00:21:43,840 --> 00:21:46,480
Speaker 13: It is more of those large language model makers who

470
00:21:46,480 --> 00:21:47,840
Speaker 13: are generating images.

471
00:21:48,720 --> 00:21:50,800
Speaker 3: Brody, thank you for joining us.

472
00:21:51,160 --> 00:22:00,080
Speaker 2: All eyes you want Adobe after the bell that.

473
00:22:00,160 --> 00:22:01,960
Speaker 3: To Neumog Technology. I'm Caroline Hyde and.

474
00:22:01,920 --> 00:22:03,520
Speaker 1: I'm Tim Staneviek in for a love.

475
00:22:03,800 --> 00:22:05,400
Speaker 2: Let's go quick check on these markets ten at the moment,

476
00:22:05,440 --> 00:22:08,560
Speaker 2: because we're currently seeing higher just when you're looking at

477
00:22:08,600 --> 00:22:11,040
Speaker 2: technology stocks and US trade more broadly. S and P

478
00:22:11,080 --> 00:22:13,000
Speaker 2: five hundred and a new record. NAZAC had a new

479
00:22:13,040 --> 00:22:15,760
Speaker 2: record two year yield. Moved by the fact that PPI

480
00:22:15,840 --> 00:22:18,800
Speaker 2: is showing once again inflatory pressure, is dialing back stock

481
00:22:18,840 --> 00:22:19,720
Speaker 2: six hundred.

482
00:22:19,400 --> 00:22:20,480
Speaker 3: Over in Europe. Just finished trade.

483
00:22:20,480 --> 00:22:23,240
Speaker 2: I'm afraid to the one point three percent downside as

484
00:22:23,240 --> 00:22:26,480
Speaker 2: we still digest political risk and maybe you know a

485
00:22:26,520 --> 00:22:30,320
Speaker 2: reparthing of where Federal Reserve pushes rates and people having

486
00:22:30,359 --> 00:22:32,080
Speaker 2: some anxiety over that. Move on, have a look at

487
00:22:32,119 --> 00:22:36,320
Speaker 2: something individual movers because record high for broadcon thirteen percent

488
00:22:36,359 --> 00:22:38,960
Speaker 2: on the nose higher. They're doing what's rather in vogue

489
00:22:38,920 --> 00:22:41,239
Speaker 2: at the moment, and a stock split by ten, so

490
00:22:41,520 --> 00:22:43,480
Speaker 2: we'll see perhaps a little bit more retail come into

491
00:22:43,520 --> 00:22:45,480
Speaker 2: this chip damaker that is also on a tear when

492
00:22:45,480 --> 00:22:48,600
Speaker 2: it comes to AI demand. Then revenue really managing to impress,

493
00:22:48,600 --> 00:22:51,280
Speaker 2: and the guidance to MicroStrategy off by six percent.

494
00:22:51,320 --> 00:22:52,400
Speaker 3: Why more supply coming?

495
00:22:52,480 --> 00:22:55,840
Speaker 2: But this term in convertible notes, why is Michael Sellers

496
00:22:56,000 --> 00:22:59,040
Speaker 2: sailor selling converts because he wants to buy mob bitcoin

497
00:22:59,680 --> 00:23:02,359
Speaker 2: and is currently up by three percent. Look that AGM

498
00:23:02,440 --> 00:23:05,120
Speaker 2: is going to be starting later today. We seem as

499
00:23:05,160 --> 00:23:07,560
Speaker 2: though shareholders are voting in favor not only of that

500
00:23:07,600 --> 00:23:09,840
Speaker 2: fifty six billion dollar pay package that was held from

501
00:23:09,880 --> 00:23:12,639
Speaker 2: twenty eighteen, but also the move to Texas. The question

502
00:23:12,840 --> 00:23:16,959
Speaker 2: is will that judge think similarly. We want to get

503
00:23:16,960 --> 00:23:19,440
Speaker 2: onto another company at the moment, and I want to

504
00:23:19,440 --> 00:23:22,720
Speaker 2: discuss what's happening over at Intuit. Intuit Mailchimp is coming

505
00:23:22,720 --> 00:23:24,600
Speaker 2: out with a new revenue tool. It's a system of

506
00:23:24,640 --> 00:23:27,640
Speaker 2: predictive and generative AI models. Basically, it's designed to help

507
00:23:27,680 --> 00:23:30,040
Speaker 2: market is win more revenue. You're going to send the

508
00:23:30,080 --> 00:23:31,840
Speaker 2: right email at the right SMS at the right time,

509
00:23:31,840 --> 00:23:34,040
Speaker 2: with the right conversion into at.

510
00:23:34,040 --> 00:23:35,159
Speaker 3: CEO is going to talk us through it.

511
00:23:35,280 --> 00:23:38,399
Speaker 2: Sasam Gourdalzi joining us for more and this is about

512
00:23:38,520 --> 00:23:41,720
Speaker 2: artificial intelligence really in the here, renowned Sasan you have

513
00:23:41,840 --> 00:23:44,399
Speaker 2: been talking up AI. You've been reworking your business as

514
00:23:44,440 --> 00:23:46,480
Speaker 2: the time you came in as CEO. I want to

515
00:23:46,880 --> 00:23:48,800
Speaker 2: you know, qdos to you for talking about it before

516
00:23:48,840 --> 00:23:50,919
Speaker 2: the rest of the market did. But how is it

517
00:23:51,000 --> 00:23:52,879
Speaker 2: actually going to generate revenue for you?

518
00:23:54,720 --> 00:23:57,280
Speaker 15: Well, first of all, thank you so much for having me.

519
00:23:58,000 --> 00:24:00,880
Speaker 15: You know, what we announced today is a really big deal.

520
00:24:00,960 --> 00:24:04,320
Speaker 15: Let me first quickly start with context and then talk

521
00:24:04,359 --> 00:24:06,280
Speaker 15: to what we launched, and how over time it will

522
00:24:06,320 --> 00:24:10,040
Speaker 15: generate revenue for into it. You know, strategically, there are

523
00:24:10,040 --> 00:24:12,679
Speaker 15: three big things really that we've declared. You know, one

524
00:24:12,880 --> 00:24:17,680
Speaker 15: is shifting from building workflows where customers do the work

525
00:24:18,080 --> 00:24:21,679
Speaker 15: to get to their financial outcomes to workflows where we

526
00:24:21,720 --> 00:24:25,040
Speaker 15: do the work for our customers. The second element is

527
00:24:25,040 --> 00:24:30,240
Speaker 15: that embedding AI powered experts human experts within every workflow

528
00:24:30,320 --> 00:24:33,440
Speaker 15: so that there's never a dead end for customers. When

529
00:24:33,480 --> 00:24:35,800
Speaker 15: there's a dead end with the technology doing the work,

530
00:24:36,440 --> 00:24:39,000
Speaker 15: are experts that actually sit on our data on AI

531
00:24:39,040 --> 00:24:43,520
Speaker 15: platform can be the assistant for consumers and small businesses.

532
00:24:43,840 --> 00:24:46,240
Speaker 15: And third, to take our game to the mid market

533
00:24:46,320 --> 00:24:50,080
Speaker 15: and small enterprises and really serve our customers in ways

534
00:24:50,080 --> 00:24:53,920
Speaker 15: that they could never imagine possible. Specifically, what we announced

535
00:24:53,920 --> 00:24:58,480
Speaker 15: today on into it mailchip platform is what we call

536
00:24:58,600 --> 00:25:01,960
Speaker 15: revenue intelligence. You know, one of the things that small

537
00:25:01,960 --> 00:25:06,480
Speaker 15: businesses care about most is revenue growth and profitability growth,

538
00:25:06,520 --> 00:25:09,560
Speaker 15: and within that their cash flow matters a lot. And

539
00:25:09,640 --> 00:25:14,040
Speaker 15: so revenue intelligence does something remarkable. It leverages all of

540
00:25:14,280 --> 00:25:19,119
Speaker 15: our customers data across Mailchimp and QuickBooks and an essence

541
00:25:19,160 --> 00:25:21,960
Speaker 15: helps them with understanding how they can drive customer and

542
00:25:22,000 --> 00:25:24,040
Speaker 15: revenue growth, So imagine swan.

543
00:25:24,200 --> 00:25:26,600
Speaker 2: I'm I'm just going to think about imagining as a

544
00:25:26,640 --> 00:25:28,600
Speaker 2: consumer here though, because I can see how this might

545
00:25:28,600 --> 00:25:32,360
Speaker 2: be music to marketiers is. But me as a consumer,

546
00:25:32,400 --> 00:25:35,880
Speaker 2: do I want more SMSs? Do I want more emails?

547
00:25:35,920 --> 00:25:37,600
Speaker 2: Do I want more marketing heading my way?

548
00:25:38,600 --> 00:25:41,639
Speaker 15: Well, if I finish the thought around this small business

549
00:25:41,640 --> 00:25:44,800
Speaker 15: and the impact that we'll have to the consumer. In essence,

550
00:25:44,800 --> 00:25:47,800
Speaker 15: the small business can understand which customers they have an

551
00:25:47,800 --> 00:25:51,440
Speaker 15: opportunity to provide more services to where they can drive

552
00:25:51,520 --> 00:25:56,359
Speaker 15: more benefits and profitability. And for a consumer, let's use

553
00:25:56,720 --> 00:25:59,240
Speaker 15: couch as an example, or new chairs as an example,

554
00:25:59,280 --> 00:26:01,240
Speaker 15: as a furniture or that you just came out with.

555
00:26:01,600 --> 00:26:06,160
Speaker 15: As a consumer of those devices or those couches or chairs,

556
00:26:06,480 --> 00:26:08,359
Speaker 15: I have an opportunity to know what's available, and I

557
00:26:08,359 --> 00:26:11,960
Speaker 15: have an opportunity to know where I can modernize my house.

558
00:26:12,040 --> 00:26:16,879
Speaker 15: And so from a consumer perspective, the benefit is significant

559
00:26:16,920 --> 00:26:20,040
Speaker 15: and consumers actually believe it or not. Ninety five percent

560
00:26:20,040 --> 00:26:22,960
Speaker 15: of consumers love to digest sms is because they're short,

561
00:26:23,000 --> 00:26:25,280
Speaker 15: they're sweet and to the point, and it has an

562
00:26:25,400 --> 00:26:29,440
Speaker 15: enormous impact for consumers and a big impact for small businesses.

563
00:26:29,480 --> 00:26:32,240
Speaker 15: And ultimately, to answer your question, the more we can

564
00:26:32,280 --> 00:26:36,119
Speaker 15: help drive revenue and profitability growth for small businesses, the

565
00:26:36,119 --> 00:26:38,560
Speaker 15: more we have the capability to monetize and be able

566
00:26:38,560 --> 00:26:41,520
Speaker 15: to drive growth into it. So we're very excited about

567
00:26:41,520 --> 00:26:42,320
Speaker 15: what we launched today.

568
00:26:42,440 --> 00:26:44,879
Speaker 4: So, Son, I want to talk about the TurboTax business

569
00:26:44,960 --> 00:26:47,879
Speaker 4: because you've spoken recently about the idea of looking for

570
00:26:47,880 --> 00:26:52,320
Speaker 4: more customers who actually use human accountants to adopt TurboTax

571
00:26:52,480 --> 00:26:54,840
Speaker 4: and not use those accounts anymore. Forgive me for getting

572
00:26:54,840 --> 00:26:57,000
Speaker 4: personal here, but there's a reason I'm asking this question.

573
00:26:57,520 --> 00:27:01,000
Speaker 4: I would imagine you have a very very complicated tax situation.

574
00:27:01,119 --> 00:27:04,240
Speaker 4: As the president and CEO of this company. Do you

575
00:27:04,440 --> 00:27:06,480
Speaker 4: use turbo tax for your taxes?

576
00:27:07,760 --> 00:27:08,359
Speaker 10: Great question.

577
00:27:08,440 --> 00:27:11,119
Speaker 15: First, let me start with answering your first question. Then

578
00:27:11,119 --> 00:27:13,960
Speaker 15: I'll answer your question about myself. You know, first and foremost,

579
00:27:14,359 --> 00:27:18,120
Speaker 15: there's a thirty five billion dollars a total addressable market,

580
00:27:18,160 --> 00:27:22,240
Speaker 15: and of that thirty five billion, thirty billion is consumers

581
00:27:22,240 --> 00:27:24,399
Speaker 15: and small businesses that actually go to somebody else to

582
00:27:24,440 --> 00:27:27,240
Speaker 15: have their taxes done. And that's really where we've built

583
00:27:27,240 --> 00:27:30,000
Speaker 15: out the platform, where it sits on a data and

584
00:27:30,040 --> 00:27:32,159
Speaker 15: AI platform where we can do your taxes for you

585
00:27:32,200 --> 00:27:35,320
Speaker 15: from a governance perspective, I do not use turbo tax

586
00:27:35,320 --> 00:27:38,400
Speaker 15: because it's important from a governance perspective that somebody else

587
00:27:38,440 --> 00:27:41,520
Speaker 15: does my taxes for me outside of our platform. So

588
00:27:41,560 --> 00:27:43,200
Speaker 15: that's the reason why I don't. I would okay to

589
00:27:43,240 --> 00:27:44,399
Speaker 15: do it because we have great experts.

590
00:27:44,480 --> 00:27:45,840
Speaker 4: What I was going to say is you, do you

591
00:27:45,880 --> 00:27:49,080
Speaker 4: ever picture a time where somebody with as complicated a

592
00:27:49,200 --> 00:27:52,720
Speaker 4: tax picture as the executive a publicly traded company can

593
00:27:52,760 --> 00:27:55,840
Speaker 4: only use a product like TurboTax for their taxes.

594
00:27:55,880 --> 00:27:57,200
Speaker 1: Does that day common? If yes?

595
00:27:57,280 --> 00:28:00,000
Speaker 10: When well, first of all, we can do that today.

596
00:28:00,760 --> 00:28:03,520
Speaker 15: If there wasn't a governance issue, I could have one

597
00:28:03,520 --> 00:28:06,040
Speaker 15: of our experts do all of my taxes today. You know,

598
00:28:06,119 --> 00:28:10,919
Speaker 15: our experts can today do anybody's taxes for them. And

599
00:28:10,960 --> 00:28:14,639
Speaker 15: it's all digital, it's all AI powered, and we have

600
00:28:14,800 --> 00:28:18,240
Speaker 15: multiple second reviews within our platform to ensure that your

601
00:28:18,280 --> 00:28:20,879
Speaker 15: taxes are done accurately and that you're either getting the

602
00:28:20,960 --> 00:28:23,359
Speaker 15: largest refund or if you have a balance due, that

603
00:28:23,400 --> 00:28:24,719
Speaker 15: you're paying the right amount.

604
00:28:24,720 --> 00:28:26,960
Speaker 10: But today, anybody can do their.

605
00:28:26,840 --> 00:28:28,919
Speaker 15: Taxes on our platform and have us do it for

606
00:28:28,960 --> 00:28:31,359
Speaker 15: them because we have all of the expertise, all of

607
00:28:31,359 --> 00:28:33,960
Speaker 15: the data and AI capabilities with the best experts in

608
00:28:34,000 --> 00:28:35,680
Speaker 15: the industry on our platform.

609
00:28:35,800 --> 00:28:39,080
Speaker 2: Yeah, we were just seeing sasan how well your share

610
00:28:39,120 --> 00:28:41,719
Speaker 2: price has been doing of late, and in many ways

611
00:28:41,960 --> 00:28:44,560
Speaker 2: of late we've seen it pop because there's a price

612
00:28:44,640 --> 00:28:47,680
Speaker 2: increase coming according to Mazooho analyst in particularly, who is

613
00:28:47,720 --> 00:28:50,320
Speaker 2: outlining what that's going to do for your revenue. Sasan,

614
00:28:50,600 --> 00:28:52,800
Speaker 2: how big an increase are we going to see for

615
00:28:52,920 --> 00:28:53,480
Speaker 2: quick books?

616
00:28:53,520 --> 00:28:54,240
Speaker 3: Can you give us a.

617
00:28:54,200 --> 00:28:57,080
Speaker 2: Sort of level revenue indeed a natural price point?

618
00:28:58,040 --> 00:28:58,280
Speaker 10: Yeah?

619
00:28:58,320 --> 00:28:58,520
Speaker 1: Sure.

620
00:28:58,560 --> 00:29:00,160
Speaker 15: I mean, first of all, I would start by saying

621
00:29:00,200 --> 00:29:04,120
Speaker 15: that we've had significant innovation on our platform in the

622
00:29:04,200 --> 00:29:07,840
Speaker 15: last year across all of the elements of our platform,

623
00:29:07,920 --> 00:29:11,040
Speaker 15: whether it's the ability to be able to fuel your success,

624
00:29:11,120 --> 00:29:14,320
Speaker 15: drive your revenue growth or profitability, and or having our

625
00:29:14,400 --> 00:29:17,680
Speaker 15: experts to help you run your business as a small business.

626
00:29:18,200 --> 00:29:21,000
Speaker 15: And we always price for value, and so this is

627
00:29:21,080 --> 00:29:23,440
Speaker 15: very much sort of in line with our pricing strategy

628
00:29:23,480 --> 00:29:27,800
Speaker 15: and our pricing principles, where we are raising prices all

629
00:29:27,840 --> 00:29:31,080
Speaker 15: the way from those that today start out to fuel

630
00:29:31,120 --> 00:29:33,400
Speaker 15: their success as a small business and follow their passion

631
00:29:33,720 --> 00:29:38,040
Speaker 15: to mid market customers. And we've gotten great feedback relative

632
00:29:38,080 --> 00:29:40,280
Speaker 15: to the innovations that we've delivered into last year, and

633
00:29:40,320 --> 00:29:43,440
Speaker 15: we're just simply pricing for value and we're excited about

634
00:29:43,440 --> 00:29:44,280
Speaker 15: the possibilities.

635
00:29:44,960 --> 00:29:47,800
Speaker 4: I just want to talk about Mint because there was

636
00:29:47,840 --> 00:29:50,480
Speaker 4: a lot of sadness when you guys shut Mint down,

637
00:29:51,040 --> 00:29:54,600
Speaker 4: and I'm wondering if you've seen those customers move over

638
00:29:54,600 --> 00:29:55,880
Speaker 4: to credit hakrmas products.

639
00:29:57,360 --> 00:29:57,520
Speaker 10: Yeah.

640
00:29:57,600 --> 00:29:59,760
Speaker 15: Well, first of all, I'll start with what Mint actually

641
00:30:00,120 --> 00:30:03,440
Speaker 15: who had served it served what we call prime customers,

642
00:30:03,440 --> 00:30:08,040
Speaker 15: those that generally have credit scores above seven hundred. And

643
00:30:08,280 --> 00:30:12,200
Speaker 15: when we thought about our vision as a consumer platform,

644
00:30:12,320 --> 00:30:15,320
Speaker 15: we want to have a self driving platform where we

645
00:30:15,360 --> 00:30:17,360
Speaker 15: can serve you early on in your life to help

646
00:30:17,400 --> 00:30:19,160
Speaker 15: you build your credit all the way to how do

647
00:30:19,200 --> 00:30:22,800
Speaker 15: you build wealth over time. And so given that vision,

648
00:30:23,000 --> 00:30:25,800
Speaker 15: we actually brought Mint and Credit Karma together so we

649
00:30:25,840 --> 00:30:28,320
Speaker 15: can serve all of our customers, from those that need

650
00:30:28,320 --> 00:30:30,920
Speaker 15: to build credit to those that have great credit scores

651
00:30:30,920 --> 00:30:34,360
Speaker 15: but also have different needs, all on one platform. We

652
00:30:34,440 --> 00:30:38,080
Speaker 15: actually had more than what we had assumed convert to

653
00:30:38,200 --> 00:30:41,360
Speaker 15: Credit Karma. In fact, the majority of our customers converted

654
00:30:41,400 --> 00:30:45,000
Speaker 15: to Credit Karma, particularly because of all the capabilities that

655
00:30:45,000 --> 00:30:47,600
Speaker 15: we have on credit Karma to be able to serve

656
00:30:47,720 --> 00:30:49,040
Speaker 15: our prime customers.

657
00:30:49,440 --> 00:30:50,160
Speaker 10: There are a.

658
00:30:50,200 --> 00:30:52,760
Speaker 15: Very small court of customers that did not convert. These

659
00:30:52,760 --> 00:30:57,360
Speaker 15: are customers that really their love is budgeting tools, budgeting goals,

660
00:30:57,360 --> 00:30:59,440
Speaker 15: and those are some of the capabilities that over time

661
00:30:59,480 --> 00:31:02,200
Speaker 15: we're building in credit Karma, but are not available today.

662
00:31:02,720 --> 00:31:05,240
Speaker 15: I would tell you some court of customers were very

663
00:31:05,240 --> 00:31:08,320
Speaker 15: sad that Mint went away, but actually the majority of

664
00:31:08,320 --> 00:31:10,720
Speaker 15: our customers are delighted to be on credit Harma.

665
00:31:10,800 --> 00:31:13,400
Speaker 2: Now I'm going to ask him off set whether he's

666
00:31:13,440 --> 00:31:16,080
Speaker 2: delighted or heartbroken, but I'm interested ss on.

667
00:31:16,160 --> 00:31:18,680
Speaker 3: Lastly, we did just get some recent news out of

668
00:31:18,720 --> 00:31:19,200
Speaker 3: the IRS.

669
00:31:19,280 --> 00:31:22,120
Speaker 2: Basically, there's going to be a free tool the US government,

670
00:31:22,120 --> 00:31:24,560
Speaker 2: in particular supporting Americans who want the option to file

671
00:31:24,720 --> 00:31:28,600
Speaker 2: directly with the IRS as ultimately to be in charge

672
00:31:28,600 --> 00:31:29,280
Speaker 2: of their own taxes.

673
00:31:29,320 --> 00:31:30,760
Speaker 3: Is this going to be a competitive threat? Are you

674
00:31:30,760 --> 00:31:32,200
Speaker 3: going to lose people to this free tool?

675
00:31:33,520 --> 00:31:35,600
Speaker 15: I mean, the short answer is no, because free is

676
00:31:35,680 --> 00:31:39,840
Speaker 15: already available to all Americans today and so with what

677
00:31:39,920 --> 00:31:43,160
Speaker 15: the IRS has launched, it's just yet another free tool.

678
00:31:43,520 --> 00:31:45,920
Speaker 15: So it is not a threat to us because freeze

679
00:31:45,880 --> 00:31:49,440
Speaker 15: are already available across all private companies. We also don't

680
00:31:49,480 --> 00:31:52,480
Speaker 15: believe that it's the best use of government money to

681
00:31:53,240 --> 00:31:55,640
Speaker 15: come out with another tool that's free where free is

682
00:31:55,680 --> 00:31:56,600
Speaker 15: already available.

683
00:31:56,920 --> 00:31:58,240
Speaker 10: It's not the best use of money.

684
00:31:58,520 --> 00:32:01,320
Speaker 15: We'd love to see more money go towards the service

685
00:32:01,360 --> 00:32:03,840
Speaker 15: that the IRS provides to take very good care of

686
00:32:04,320 --> 00:32:06,959
Speaker 15: the citizens. But nevertheless, as far as the threat for us,

687
00:32:06,960 --> 00:32:07,560
Speaker 15: it really.

688
00:32:07,400 --> 00:32:10,920
Speaker 4: Is not into its CEO Sosan Ga Darzi Sossan, thanks

689
00:32:10,920 --> 00:32:11,840
Speaker 4: so much for joining us.

690
00:32:11,840 --> 00:32:12,520
Speaker 1: Appreciate it.

691
00:32:12,600 --> 00:32:13,440
Speaker 10: Thank you for having me.

692
00:32:13,520 --> 00:32:15,200
Speaker 4: Well, coming up, we're going to be joined by Amber

693
00:32:15,240 --> 00:32:18,600
Speaker 4: Atherton from Patron to talk all things gaming and VC.

694
00:32:18,760 --> 00:32:19,320
Speaker 10: That's next.

695
00:32:19,480 --> 00:32:21,080
Speaker 1: This is Bloomberg.

696
00:32:30,560 --> 00:32:31,840
Speaker 3: Now in today's VC Spotlight.

697
00:32:31,880 --> 00:32:34,920
Speaker 2: Let's talk about the health of the video game industry,

698
00:32:35,040 --> 00:32:38,200
Speaker 2: of social networks, of groupings, and whether we might see

699
00:32:38,200 --> 00:32:40,320
Speaker 2: actually an optic in investment in the second half of

700
00:32:40,360 --> 00:32:43,719
Speaker 2: the year into these sorts of processes. Amburatherton's with us

701
00:32:43,840 --> 00:32:47,880
Speaker 2: partner at the early stage at benufirm Patron, and you're

702
00:32:47,920 --> 00:32:52,480
Speaker 2: all in on sort of networks of people, of thinking

703
00:32:52,560 --> 00:32:54,880
Speaker 2: and of gaming interaction online.

704
00:32:54,920 --> 00:32:56,240
Speaker 3: Having been a founder now a VC.

705
00:32:56,880 --> 00:32:59,520
Speaker 2: What is artificial intelligence doing to the conversations you're having

706
00:32:59,560 --> 00:33:00,000
Speaker 2: at the moment?

707
00:33:01,440 --> 00:33:03,680
Speaker 16: Yeah, I mean we're good to be back. Thanks for

708
00:33:03,720 --> 00:33:06,360
Speaker 16: having me. I think after selling my startup to Discord,

709
00:33:06,520 --> 00:33:09,880
Speaker 16: I saw firsthand that this younger generation really see gaming

710
00:33:10,160 --> 00:33:13,160
Speaker 16: not just as entertainment, but as a social networks where

711
00:33:13,200 --> 00:33:15,480
Speaker 16: they hang out with their friends. And I think you're

712
00:33:15,520 --> 00:33:19,680
Speaker 16: seeing us move towards a more community driven multiplayer internet,

713
00:33:19,760 --> 00:33:22,480
Speaker 16: and so I think when you see AI as part

714
00:33:22,480 --> 00:33:24,480
Speaker 16: of that story, I mean if you look at Nvidio,

715
00:33:24,520 --> 00:33:27,160
Speaker 16: you look at Microsoft, all of these are companies that

716
00:33:27,240 --> 00:33:30,000
Speaker 16: started out serving the gaming market, and it's not a

717
00:33:30,040 --> 00:33:33,760
Speaker 16: surprise that they now have an edge on AI. Because

718
00:33:34,000 --> 00:33:36,520
Speaker 16: they start out serving gamers who are very early adopters

719
00:33:36,560 --> 00:33:39,040
Speaker 16: to new platforms, they have huge demands in terms of

720
00:33:39,080 --> 00:33:40,320
Speaker 16: graphics and infrastructure.

721
00:33:40,640 --> 00:33:42,360
Speaker 3: So I think we're very excited about startups that.

722
00:33:42,320 --> 00:33:46,840
Speaker 16: Are using gaming as a wedge to further improve AI.

723
00:33:47,200 --> 00:33:48,360
Speaker 4: Wally and Brot, I want to talk about one of

724
00:33:48,400 --> 00:33:51,480
Speaker 4: your most recent investments. Caroline and I were talking about

725
00:33:51,480 --> 00:33:54,960
Speaker 4: it a little earlier. The company Aria just announced yesterday.

726
00:33:55,640 --> 00:33:58,680
Speaker 4: It's interesting because AI sort of goes into the background

727
00:33:58,680 --> 00:34:02,000
Speaker 4: there with sort of this AI powered concierge. Talk a

728
00:34:02,040 --> 00:34:05,480
Speaker 4: little bit about what exactly this investment is because it's

729
00:34:05,560 --> 00:34:08,399
Speaker 4: not completely in the world of gaming, as in your

730
00:34:08,560 --> 00:34:11,200
Speaker 4: playing games against somebody else in a virtual world, but

731
00:34:11,239 --> 00:34:14,720
Speaker 4: it actually takes a real world approach right exactly.

732
00:34:14,760 --> 00:34:17,200
Speaker 16: And I think part of Patron's thesis is that gaming

733
00:34:17,280 --> 00:34:20,080
Speaker 16: can be a source of good and better goodness for

734
00:34:20,160 --> 00:34:22,960
Speaker 16: the world. And so I think when we're looking at gaming,

735
00:34:23,000 --> 00:34:25,239
Speaker 16: it's from a broader lens of yes, we have mets

736
00:34:25,280 --> 00:34:27,840
Speaker 16: and studios, but we also look at how gaming is

737
00:34:27,880 --> 00:34:31,240
Speaker 16: influencing border consumer technology and Aria is a good example

738
00:34:31,239 --> 00:34:35,480
Speaker 16: of this where AI doesn't have to completely replace human interaction,

739
00:34:35,640 --> 00:34:38,719
Speaker 16: and in fact, what Aria is doing is helping couples

740
00:34:39,400 --> 00:34:42,640
Speaker 16: increase their relationship strength be better partners to each other

741
00:34:42,760 --> 00:34:47,000
Speaker 16: through this wonderful hybrid of an AI concierge, physical boxes,

742
00:34:47,040 --> 00:34:49,000
Speaker 16: and a lot of content. So we look at it

743
00:34:49,040 --> 00:34:51,680
Speaker 16: a much border lens, and I think companies like Aria

744
00:34:51,840 --> 00:34:54,279
Speaker 16: have a really great shot at using AI in a

745
00:34:54,280 --> 00:34:56,040
Speaker 16: way that doesn't isolate human connection.

746
00:34:57,000 --> 00:35:01,160
Speaker 2: Digital human beings is the the to surround Alterra a

747
00:35:01,160 --> 00:35:05,279
Speaker 2: different portfolio company of yours, and you're investing alongside the

748
00:35:05,360 --> 00:35:08,000
Speaker 2: likes of Eric Schmid in this one particular, it's about

749
00:35:08,239 --> 00:35:10,440
Speaker 2: lab building agents and how does that fit into a

750
00:35:10,440 --> 00:35:13,480
Speaker 2: gaming context. Are you're going to be gaming alongside agents.

751
00:35:13,520 --> 00:35:15,520
Speaker 2: Are they going to be within your in the world's

752
00:35:15,520 --> 00:35:16,840
Speaker 2: ones creating.

753
00:35:17,640 --> 00:35:20,360
Speaker 16: Yeah, I think it's very rare as an investor to

754
00:35:20,440 --> 00:35:23,520
Speaker 16: witness a paradigm shift in terms of how humans interact

755
00:35:23,560 --> 00:35:26,000
Speaker 16: with computers. So when we met the founders of al Terra,

756
00:35:26,400 --> 00:35:29,000
Speaker 16: we were very excited about what they were building. You know,

757
00:35:29,239 --> 00:35:32,239
Speaker 16: in the initial phases, it's very much you're able to

758
00:35:32,320 --> 00:35:35,080
Speaker 16: game with an AI agent instead of a friend or

759
00:35:35,120 --> 00:35:38,879
Speaker 16: with a friend, But where that's ultimately going is toward.

760
00:35:38,760 --> 00:35:40,000
Speaker 3: Digital human beings.

761
00:35:40,040 --> 00:35:42,480
Speaker 16: So I don't think it's inconceivable to think that in

762
00:35:42,480 --> 00:35:46,400
Speaker 16: the future we will have AI agents and AI friends

763
00:35:46,400 --> 00:35:50,560
Speaker 16: that provide more than utility book companionship, friendship, and you're

764
00:35:50,600 --> 00:35:53,040
Speaker 16: able to play play games with them. They're able to

765
00:35:53,080 --> 00:35:55,520
Speaker 16: assist you in your life. And so I think al

766
00:35:55,600 --> 00:35:58,560
Speaker 16: Terra is a very interesting example of using gaming as

767
00:35:58,600 --> 00:36:02,080
Speaker 16: a wedge to ultimately the much much border consumer market.

768
00:36:02,640 --> 00:36:07,560
Speaker 2: But you wrote a book about online communities. They're real

769
00:36:07,640 --> 00:36:12,760
Speaker 2: people having real relationships via internet. Now we're thinking about

770
00:36:12,760 --> 00:36:16,759
Speaker 2: how we're going to be playing against totally false agents

771
00:36:16,840 --> 00:36:19,080
Speaker 2: who are not actually real humans, and we're thinking about

772
00:36:19,120 --> 00:36:22,640
Speaker 2: building our own intimacy via bets that you're making through

773
00:36:22,640 --> 00:36:25,080
Speaker 2: the power of AI. Are you feeling comfortable with the

774
00:36:25,120 --> 00:36:27,839
Speaker 2: direction of travel of artificial intelligence and humanity at the moment?

775
00:36:27,880 --> 00:36:31,600
Speaker 2: Do you think it's ultimately helping or hindering in some way?

776
00:36:32,960 --> 00:36:35,879
Speaker 16: I'm, you know, in the business of optimism, So for me,

777
00:36:36,200 --> 00:36:41,360
Speaker 16: it's that AI is not replacing humans. AI is augmenting humans.

778
00:36:41,440 --> 00:36:44,680
Speaker 16: AI is bringing a new dimension of fun and personalization

779
00:36:44,760 --> 00:36:47,960
Speaker 16: into so many different consumer interactions. And I think for

780
00:36:48,120 --> 00:36:51,239
Speaker 16: us as patron, you know, we invest across this spectrum

781
00:36:51,280 --> 00:36:53,319
Speaker 16: of play, and I think it's very difficult to see

782
00:36:53,320 --> 00:36:56,160
Speaker 16: where consumer technology and AI is going if you don't

783
00:36:56,280 --> 00:37:00,560
Speaker 16: understand gaming. So for us, we are excited about founders

784
00:37:00,600 --> 00:37:03,800
Speaker 16: that are operating at the earlier stages, you know, seed stage,

785
00:37:03,880 --> 00:37:06,640
Speaker 16: and are using AI to really be a force of

786
00:37:06,680 --> 00:37:07,440
Speaker 16: good in the world.

787
00:37:07,800 --> 00:37:10,640
Speaker 4: Amber who's the platform winner in the world that you

788
00:37:10,800 --> 00:37:14,560
Speaker 4: envision in the coming years? Is it iOS? Is it Android?

789
00:37:15,160 --> 00:37:18,880
Speaker 4: Is it Nintendo? Is it Microsoft? Is it Sony?

790
00:37:19,000 --> 00:37:20,760
Speaker 1: Or is it somebody we're not even talking about.

791
00:37:21,600 --> 00:37:24,040
Speaker 16: I think you've mentioned a lot of very interesting names

792
00:37:24,080 --> 00:37:27,120
Speaker 16: there who are all experimenting at the forefront of you know,

793
00:37:27,239 --> 00:37:30,799
Speaker 16: different technological edges. Whether it is ARVR, which we talked

794
00:37:30,840 --> 00:37:33,160
Speaker 16: a lot about last time, Caroline, or whether it is

795
00:37:33,239 --> 00:37:36,840
Speaker 16: AI and so I think that looking at the gaming

796
00:37:36,880 --> 00:37:39,280
Speaker 16: stocks particularly, you know a discord if you look at Reddit,

797
00:37:39,320 --> 00:37:42,000
Speaker 16: one of the most oversubscribed IPOs, and actually even a

798
00:37:42,000 --> 00:37:45,960
Speaker 16: company like web Tunes that is adjacent to gaming, those

799
00:37:45,960 --> 00:37:48,880
Speaker 16: types of platforms are very very interesting in terms of

800
00:37:48,880 --> 00:37:51,080
Speaker 16: where the next generation is spending their time.

801
00:37:51,120 --> 00:37:53,200
Speaker 3: You know, they're growing up on games.

802
00:37:53,480 --> 00:37:55,880
Speaker 16: Half of us eight to ten year olds playing Roadblox

803
00:37:55,880 --> 00:37:58,880
Speaker 16: every single day. So I think looking at those stocks

804
00:37:58,880 --> 00:38:03,759
Speaker 16: and those gaming companies is where you'll see the platform breakouts.

805
00:38:03,480 --> 00:38:04,839
Speaker 1: And we're going to have to leave it there.

806
00:38:04,880 --> 00:38:08,560
Speaker 4: That's patron partner Amber Atherton joining us from California, Thanks

807
00:38:08,560 --> 00:38:08,879
Speaker 4: so much.

808
00:38:16,320 --> 00:38:16,520
Speaker 1: Now.

809
00:38:16,520 --> 00:38:20,920
Speaker 2: On Wednesday, fired SpaceX engineers filed a lawsuit against Eno

810
00:38:21,000 --> 00:38:23,880
Speaker 2: Musk for sexual harassment and retaliation. That was over in

811
00:38:23,880 --> 00:38:27,080
Speaker 2: a California state court. Now this escalates their multi front

812
00:38:27,160 --> 00:38:30,640
Speaker 2: legal battle with the billionaire chief executive and his aerospace company. Now,

813
00:38:30,640 --> 00:38:35,600
Speaker 2: the Calllifornia suit claims the Musk posted sexual photographs, demeaning commentary,

814
00:38:35,880 --> 00:38:39,560
Speaker 2: and chose to fire employees for speaking up. Josh Idelson

815
00:38:39,680 --> 00:38:42,400
Speaker 2: joins us now for more to discuss eight employees here,

816
00:38:42,880 --> 00:38:44,960
Speaker 2: and this isn't the first time we've heard.

817
00:38:44,840 --> 00:38:46,760
Speaker 1: From them, that's right.

818
00:38:46,920 --> 00:38:50,000
Speaker 17: This is a case from twenty twenty two in which,

819
00:38:50,000 --> 00:38:55,200
Speaker 17: as you said, workers engineers say that because they worked

820
00:38:55,239 --> 00:38:59,000
Speaker 17: on an open letter raising concerns about the company's culture

821
00:38:59,000 --> 00:39:03,399
Speaker 17: and Musk's behavior, or asking whether the company's policies were

822
00:39:03,440 --> 00:39:07,520
Speaker 17: being applied equally to the chief executive, asking whether this

823
00:39:07,680 --> 00:39:09,360
Speaker 17: was the kind of culture they wanted to bring to

824
00:39:09,400 --> 00:39:13,160
Speaker 17: mars that as a result, they were fired. They brought

825
00:39:13,160 --> 00:39:15,759
Speaker 17: a case to the US Labor Board, which issued a

826
00:39:15,760 --> 00:39:19,200
Speaker 17: complaint saying they were illegally retaliated against in violation of

827
00:39:19,239 --> 00:39:22,960
Speaker 17: federal labor law. Now that case at the Labor Board

828
00:39:23,320 --> 00:39:27,520
Speaker 17: is essentially frozen by a federal court injunction because SpaceX

829
00:39:27,960 --> 00:39:31,200
Speaker 17: responded by accusing the US Labor Board of having an

830
00:39:31,239 --> 00:39:36,120
Speaker 17: unconstitutional structure. So now this is a different front where

831
00:39:36,520 --> 00:39:40,600
Speaker 17: these workers are saying that the company also violated California's

832
00:39:40,600 --> 00:39:44,160
Speaker 17: state law. And in this case they are able to

833
00:39:44,280 --> 00:39:48,320
Speaker 17: name Elon Musk personally as a defendant. They're also able

834
00:39:48,360 --> 00:39:51,399
Speaker 17: to seek damages that the Labor Board can't.

835
00:39:52,480 --> 00:39:54,760
Speaker 2: Now we want to get to the response from SpaceX

836
00:39:54,760 --> 00:39:57,040
Speaker 2: and eal mask. They've not immediately responded to request to

837
00:39:57,120 --> 00:40:00,719
Speaker 2: comment on the lawsuit. They previously denied though rom doing

838
00:40:00,719 --> 00:40:04,319
Speaker 2: as you said and said the fired employees violated policies

839
00:40:04,840 --> 00:40:07,920
Speaker 2: and that Musk was actually not involved in their termination.

840
00:40:08,760 --> 00:40:12,520
Speaker 2: But ultimately, have we seen responses coming from the business

841
00:40:12,520 --> 00:40:15,520
Speaker 2: more coordinated fashion to try and tackle at least some

842
00:40:15,560 --> 00:40:19,880
Speaker 2: of the issues that are now being circulated amongst citizens

843
00:40:19,880 --> 00:40:21,240
Speaker 2: as well as those within the company.

844
00:40:21,760 --> 00:40:25,000
Speaker 17: Well, we've seen in the Wall Street Journal, for example,

845
00:40:25,239 --> 00:40:29,239
Speaker 17: Gwen Shotwell, the company's president, saying that allegations that have

846
00:40:29,280 --> 00:40:32,880
Speaker 17: been made don't reflect the culture at the company. The

847
00:40:33,000 --> 00:40:37,040
Speaker 17: lawsuit that these fired engineers filed seeks to get a

848
00:40:37,160 --> 00:40:40,600
Speaker 17: court to force the company to change its policies to

849
00:40:40,680 --> 00:40:44,400
Speaker 17: address what they say is the company and the CEO

850
00:40:45,160 --> 00:40:49,960
Speaker 17: knowingly creating a hostile work environment and retaliating against people

851
00:40:50,360 --> 00:40:53,960
Speaker 17: for bringing it up. They note that Musk has publicly

852
00:40:54,040 --> 00:40:57,440
Speaker 17: marked misconduct allegations against him in the past.

853
00:40:57,840 --> 00:41:01,440
Speaker 4: What could the implications be for Space if these engineers

854
00:41:01,719 --> 00:41:04,960
Speaker 4: are successful in their lawsuit and the implications for Elon

855
00:41:05,040 --> 00:41:06,120
Speaker 4: Musk as well.

856
00:41:06,239 --> 00:41:08,160
Speaker 10: Well. They're seeking damages.

857
00:41:08,480 --> 00:41:12,840
Speaker 17: They're seeking in an injunction that would force the company

858
00:41:12,880 --> 00:41:16,600
Speaker 17: to change its behavior. One question here is how much

859
00:41:17,280 --> 00:41:21,719
Speaker 17: the entities that SpaceX contracts with care about this, including

860
00:41:22,040 --> 00:41:25,560
Speaker 17: the US government. Elon Musk has shown a lot of

861
00:41:25,560 --> 00:41:30,279
Speaker 17: tolerance for controversy and for legal battle and for appeals,

862
00:41:30,360 --> 00:41:33,319
Speaker 17: as we've seen in a number of cases. We will

863
00:41:33,320 --> 00:41:38,120
Speaker 17: see if the company chooses to change anything in response

864
00:41:38,239 --> 00:41:40,000
Speaker 17: to this escalating controverty.

865
00:41:40,040 --> 00:41:42,360
Speaker 4: Does it have any implications for Tesla or any of

866
00:41:42,480 --> 00:41:43,840
Speaker 4: Elon Musk's other companies.

867
00:41:44,160 --> 00:41:44,399
Speaker 7: Well.

868
00:41:44,520 --> 00:41:47,160
Speaker 17: People who've come forward with allegations have said that they

869
00:41:47,200 --> 00:41:50,560
Speaker 17: see patterns at each of these places in terms of

870
00:41:50,600 --> 00:41:56,560
Speaker 17: the company's behavior and in terms of the CEO's willingness

871
00:41:56,640 --> 00:42:01,160
Speaker 17: to continue taking the approach that he's taken, even in

872
00:42:01,200 --> 00:42:06,040
Speaker 17: the face of public embarrassment and allegations that the company

873
00:42:06,080 --> 00:42:07,360
Speaker 17: again is denied wrongdoing.

874
00:42:07,880 --> 00:42:11,120
Speaker 4: Bloomberg Josh Idolson here in New York. Josh, good to

875
00:42:11,120 --> 00:42:13,239
Speaker 4: see you. Thanks for joining us on this to appreciate it.

876
00:42:13,239 --> 00:42:13,600
Speaker 10: Thank you.

877
00:42:14,160 --> 00:42:16,319
Speaker 2: Great to have him in the studio meanwhile, But that

878
00:42:16,400 --> 00:42:19,320
Speaker 2: does it for this edition of Bloomberg Technology. You don't

879
00:42:19,400 --> 00:42:21,440
Speaker 2: want to forget to check out our podcast. You can

880
00:42:21,480 --> 00:42:23,600
Speaker 2: find it on the terminal as well as online on Apple,

881
00:42:23,640 --> 00:42:25,640
Speaker 2: on Spotify, and I heart Tim. It's great have you

882
00:42:25,680 --> 00:42:27,120
Speaker 2: sat next to me. And there's so much more to

883
00:42:27,160 --> 00:42:28,520
Speaker 2: digestoing for in the rest of the day.

884
00:42:28,520 --> 00:42:29,640
Speaker 1: Should we do this again tomorrow?

885
00:42:29,800 --> 00:42:30,000
Speaker 2: Yeah?

886
00:42:30,080 --> 00:42:30,560
Speaker 3: Baby, night?

887
00:42:30,600 --> 00:42:32,080
Speaker 10: Okay, cool, good stop by here.

888
00:42:32,080 --> 00:42:33,600
Speaker 3: I'll be here to make your day a little bit longer.

889
00:42:33,840 --> 00:42:35,040
Speaker 1: I'll see what will be.

890
00:42:35,000 --> 00:42:37,560
Speaker 2: Watching the all important Tesla AGM a little bit later.

891
00:42:37,640 --> 00:42:38,319
Speaker 3: Stay tuned for it.

892
00:42:38,680 --> 00:42:43,360
Speaker 8: This is Blomberg Technology.